{"id":"W2521745822","doi":"10.5539/ijel.v6n5p1","title":"Predicting New TV Series Ratings from their Pilot Episode Scripts","year":2016,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Television series; Scripting language; Key (lock); Sample (material); Computer science; Test (biology); Measure (data warehouse); Econometrics; Psychology; Statistics; Mathematics; Data mining; Media studies; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009021204,0.0002179671,0.0001510031,0.001003477,0.0001772405,0.0009950162,0.000196242,0.0003123462,0.005387247],"category_scores_gemma":[0.009300891,0.0001571071,0.0002122414,0.0006148273,0.0001975175,0.0008411022,0.0004176356,0.0004669137,0.0007291054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003016953,"about_ca_system_score_gemma":0.0001262176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00466173,"about_ca_topic_score_gemma":0.006848427,"domain_scores_codex":[0.9996924,0.0001312394,0.00002522221,0.000055595,0.00006135183,0.00003405228],"domain_scores_gemma":[0.9908335,0.004764932,0.002380267,0.0003565209,0.0006484498,0.001016373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001096129,0.00007343643,0.9901178,0.00001511546,0.00002718457,0.00006131503,0.0006169489,0.0008562648,0.0007523921,0.0001627814,0.0004362542,0.006770809],"study_design_scores_gemma":[0.000005389326,0.00007111439,0.9871675,0.000007068837,0.00001463146,0.00005028401,0.0007042977,0.01112144,0.0002686308,0.00008600419,0.0004980307,0.000005553846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979754,0.00002531609,0.000306119,0.00002882455,0.00000294692,0.00001209543,0.0002214996,0.000009074522,0.001418638],"genre_scores_gemma":[0.9989101,0.00002828944,0.000203663,0.000003995369,0.000008857057,0.0000108521,0.0004635463,0.000003844592,0.000366909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005387247,"threshold_uncertainty_score":0.01802212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03460884723369303,"score_gpt":0.3115358884100882,"score_spread":0.2769270411763952,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}